On the benefits of region of interest detection for whole slide image classification

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buir.contributor.authorKorkut, Sena
buir.contributor.authorErkan, Cihan
buir.contributor.authorAksoy, Selim
dc.citation.epage124710N-9en_US
dc.citation.spage124710N-1
dc.citation.volumeNumber12471
dc.contributor.authorKorkut, Sena
dc.contributor.authorErkan, Cihan
dc.contributor.authorAksoy, Selim
dc.contributor.editorTomaszewski, John E.
dc.contributor.editorWard, Aaron D.
dc.coverage.spatialSan Diego, California, United States
dc.date.accessioned2024-03-07T08:43:53Z
dc.date.available2024-03-07T08:43:53Z
dc.date.issued2023-04-06
dc.departmentDepartment of Computer Engineering
dc.descriptionConference Name: SPIE Medical Imaging, 2023
dc.descriptionDate of Conference: 19-24 February 2023
dc.description.abstractWhole slide image (WSI) classification methods typically use fixed-size patches that are processed separately and are aggregated for the final slide-level prediction. Image segmentation methods are designed to obtain a delineation of specific tissue types. These two tasks are usually studied independently. The aim of this work is to investigate the effect of region of interest (ROI) detection as a preliminary step for WSI classification. First, we process each WSI by using a pixel-level classifier that provides a binary segmentation mask for potentially important ROIs. We evaluate both single-resolution models that process each magnification independently and multi-resolution models that simultaneously incorporate contextual information and local details. Then, we compare the WSI classification performances of patch-based models when the patches used for both training and testing are extracted from the whole image and when they are sampled from only within the detected ROIs. The experiments using a binary classification setting for breast histopathology slides as benign vs. malignant show that the classifier that uses the patches sampled from the whole image achieves an F1 score of 0.68 whereas the classifiers that use patches sampled from the ROI detection results produced by the single- and multi-resolution models obtain scores between 0.75 and 0.83.
dc.description.provenanceMade available in DSpace on 2024-03-07T08:43:53Z (GMT). No. of bitstreams: 1 On_the_benefits_of_region_of_interest_detection_for_whole_slide_image_classification.pdf: 20437616 bytes, checksum: af2fec3a63222e1c709377e102ec7893 (MD5) Previous issue date: 2023-04-06en
dc.identifier.doi10.1117/12.2654193
dc.identifier.issn1605-7422
dc.identifier.urihttps://hdl.handle.net/11693/114380
dc.language.isoen_US
dc.publisherSPIE
dc.relation.isversionofhttps://doi.org/10.1117/12.2654193
dc.source.titleProgress in Biomedical Optics and Imaging - Proceedings of SPIE
dc.subjectDigital pathology
dc.subjectBreast histopathology
dc.subjectRegion of interest detection
dc.subjectWhole slide image classification
dc.subjectMulti-resolution image analysis
dc.titleOn the benefits of region of interest detection for whole slide image classification
dc.typeConference Paper

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